Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization
暂无分享,去创建一个
C. Davatzikos | A. Alexander-Bloch | R. Shinohara | T. Satterthwaite | Meichen Yu | H. Shou | Andrew A. Chen | Hannah Horng | V. Bashyam | Fengling Hu | Mingyao Li
[1] Chunshui Yu,et al. Four Distinct Subtypes of Alzheimer’s Disease Based on Resting-State Connectivity Biomarkers , 2022, Biological Psychiatry.
[2] Thomas Christen,et al. ImUnity: a generalizable VAE-GAN solution for multicenter MR image harmonization , 2021, Medical Image Analysis.
[3] C. Cavaliere,et al. A Framework of Analysis to Facilitate the Harmonization of Multicenter Radiomic Features in Prostate Cancer , 2022, Journal of clinical medicine.
[4] M. Mazurowski,et al. Deep Learning for Breast MRI Style Transfer with Limited Training Data , 2022, Journal of Digital Imaging.
[5] Rhea D. Chitalia,et al. Resampling and harmonization for mitigation of heterogeneity in image parameters of baseline scans , 2022, Scientific reports.
[6] Jinwoo Hong,et al. General psychopathology factor (p-factor) prediction using resting-state functional connectivity and a scanner-generalization neural network. , 2022, Journal of psychiatric research.
[7] G. Williams,et al. Validation of cross-sectional and longitudinal ComBat harmonization methods for magnetic resonance imaging data on a travelling subject cohort , 2022, Neuroimage. Reports.
[8] R. Bateman,et al. Lecanemab in Early Alzheimer's Disease. , 2022, The New England journal of medicine.
[9] Eric A. Cohen,et al. Improved generalized ComBat methods for harmonization of radiomic features , 2022, Scientific Reports.
[10] Georgios C. Manikis,et al. Harmonization Strategies in Multicenter MRI-Based Radiomics , 2022, J. Imaging.
[11] N. Jahanshad,et al. Site effects how-to and when: An overview of retrospective techniques to accommodate site effects in multi-site neuroimaging analyses , 2022, Frontiers in Neurology.
[12] G. Logroscino,et al. The impact of harmonization on radiomic features in Parkinson’s disease and healthy controls: A multicenter study , 2022, Frontiers in Neuroscience.
[13] Bruno M. de Brito Robalo,et al. Improved sensitivity and precision in multicentre diffusion MRI network analysis using thresholding and harmonization , 2022, NeuroImage: Clinical.
[14] M. Battaglini,et al. Multicenter data harmonization for regional brain atrophy and application in multiple sclerosis , 2022, Journal of Neurology.
[15] Weili Lin,et al. Fast Image-Level MRI Harmonization via Spectrum Analysis , 2022, MLMI@MICCAI.
[16] C. Delrieux,et al. Assessing Multi-Site rs-fMRI-Based Connectomic Harmonization Using Information Theory , 2022, Brain sciences.
[17] A. Voineskos,et al. A structured multivariate approach for removal of latent batch effects , 2022 .
[18] Y. Li,et al. The alterations of brain functional connectivity networks in major depressive disorder detected by machine learning through multisite rs-fMRI data , 2022, Behavioural Brain Research.
[19] Zhao Liu,et al. Style transfer in conditional GANs for cross-modality synthesis of brain magnetic resonance images , 2022, Comput. Biol. Medicine.
[20] B. Yeo,et al. Goal-specific brain MRI harmonization , 2022, NeuroImage.
[21] L. Concha,et al. Structural network alterations in focal and generalized epilepsy assessed in a worldwide ENIGMA study follow axes of epilepsy risk gene expression , 2022, Nature Communications.
[22] R. Sun,et al. AutoComBat: a generic method for harmonizing MRI-based radiomic features , 2022, Scientific Reports.
[23] Mahbaneh Eshaghzadeh Torbati,et al. ComBat Harmonization: Empirical Bayes versus Fully Bayes Approaches , 2022, bioRxiv.
[24] Chunshui Yu,et al. Environmental neuroscience linking exposome to brain structure and function underlying cognition and behavior , 2022, Molecular Psychiatry.
[25] V. Vergara,et al. Multi-Site Mild Traumatic Brain Injury Classification with Machine Learning and Harmonization , 2022, 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).
[26] Suheyla Cetin Karayumak,et al. Cross-site harmonization of multi-shell diffusion MRI measures based on rotational invariant spherical harmonics (RISH) , 2022, NeuroImage.
[27] S. Nie,et al. Self-supervised learning for multi-center magnetic resonance imaging harmonization without traveling phantoms , 2022, Physics in medicine and biology.
[28] Adrian I. Onicas,et al. Multisite Harmonization of Structural DTI Networks in Children: An A-CAP Study , 2022, Frontiers in Neurology.
[29] D. Na,et al. Harmonization of Multicenter Cortical Thickness Data by Linear Mixed Effect Model , 2022, Frontiers in Aging Neuroscience.
[30] A. Malhotra,et al. A longitudinal multi-scanner multimodal human neuroimaging dataset , 2022, Scientific Data.
[31] R. Bellotti,et al. Multi-site harmonization of MRI data uncovers machine-learning discrimination capability in barely separable populations: An example from the ABIDE dataset , 2022, NeuroImage: Clinical.
[32] Colin B. Hansen,et al. Contrastive semi-supervised harmonization of single-shell to multi-shell diffusion MRI. , 2022, Magnetic resonance imaging.
[33] C. Lebel,et al. Lifespan Volume Trajectories From Non–harmonized T1–Weighted MRI Do Not Differ After Site Correction Based on Traveling Human Phantoms , 2022, Frontiers in Neurology.
[34] Iyad Ba Gari,et al. Multisite test–retest reliability and compatibility of brain metrics derived from FreeSurfer versions 7.1, 6.0, and 5.3 , 2022, bioRxiv.
[35] Jerry L Prince,et al. Evaluating the impact of MR image harmonization on thalamus deep network segmentation , 2022, Medical Imaging.
[36] Anup Tuladhar,et al. Lesion-preserving unpaired image-to-image translation between MRI and CT from ischemic stroke patients , 2022, Medical Imaging.
[37] A. Scarsbrook,et al. Harmonisation of scanner-dependent contrast variations in magnetic resonance imaging for radiation oncology, using style-blind auto-encoders , 2022, Physics and imaging in radiation oncology.
[38] A. Hussain,et al. A Novel 3D Unsupervised Domain Adaptation Framework for Cross-Modality Medical Image Segmentation , 2022, IEEE Journal of Biomedical and Health Informatics.
[39] Timothy O. Laumann,et al. Reproducible brain-wide association studies require thousands of individuals , 2022, Nature.
[40] Sharyn I. Katz,et al. Generalized ComBat harmonization methods for radiomic features with multi-modal distributions and multiple batch effects , 2022, Scientific Reports.
[41] Daniel P. Kennedy,et al. Video‐evoked fMRI BOLD responses are highly consistent across different data acquisition sites , 2022, Human brain mapping.
[42] V. Calhoun,et al. A Decentralized ComBat Algorithm and Applications to Functional Network Connectivity , 2022, Frontiers in Neurology.
[43] G. Venkatasubramanian,et al. Sample size requirement for achieving multisite harmonization using structural brain MRI features , 2022, NeuroImage.
[44] J. Qiu,et al. Connectome gradient dysfunction in major depression and its association with gene expression profiles and treatment outcomes , 2022, Molecular Psychiatry.
[45] H. Vargas,et al. Impact of ComBat Harmonization on PET Radiomics-Based Tissue Classification: A Dual-Center PET/MRI and PET/CT Study , 2022, The Journal of Nuclear Medicine.
[46] O. Andreassen,et al. In vivo white matter microstructure in adolescents with early-onset psychosis: a multi-site mega-analysis , 2022, Molecular Psychiatry.
[47] M. Battaglini,et al. MAGNIMS recommendations for harmonization of MRI data in MS multicenter studies , 2022, NeuroImage: Clinical.
[48] Víctor M. Campello,et al. Minimising multi-centre radiomics variability through image normalisation: a pilot study , 2022, Scientific Reports.
[49] S. Ken,et al. Radiomics-Based Detection of Radionecrosis Using Harmonized Multiparametric MRI , 2022, Cancers.
[50] Yong He,et al. A deep learning-based multisite neuroimage harmonization framework established with a traveling-subject dataset , 2021, NeuroImage.
[51] D. Bassett,et al. Harmonizing functional connectivity reduces scanner effects in community detection , 2021, NeuroImage.
[52] Evan M. Gordon,et al. A comparison of methods to harmonize cortical thickness measurements across scanners and sites , 2021, NeuroImage.
[53] A. L. Ware,et al. Harmonisation of multi-site MRS data with ComBat , 2021, bioRxiv.
[54] R. Shinohara,et al. Privacy-preserving harmonization via distributed ComBat , 2021, NeuroImage.
[55] Dan J Stein,et al. Brain charts for the human lifespan , 2021, Nature.
[56] A. Marquand,et al. Accommodating Site Variation In Neuroimaging Data Using Normative And Hierarchical Bayesian Models , 2021, NeuroImage.
[57] J. Thiran,et al. Neuroimaging Harmonization Using cGANs: Image Similarity Metrics Poorly Predict Cross-Protocol Volumetric Consistency , 2022, MLCN@MICCAI.
[58] Seong Jae Hwang,et al. MISPEL: A deep learning approach for harmonizing multi-scanner matched neuroimaging data , 2022 .
[59] Justin C. Park,et al. Deep Learning in Large and Multi-Site Structural Brain MR Imaging Datasets , 2022, Frontiers in Neuroinformatics.
[60] Joanne C. Beer,et al. Mitigating site effects in covariance for machine learning in neuroimaging data , 2021, Human brain mapping.
[61] J. Vogelstein,et al. Moving Beyond Processing and Analysis-Related Variation in Neuroscience , 2021, bioRxiv.
[62] Alexandra M. Reardon,et al. Improving Between-Group Effect Size for Multi-Site Functional Connectivity Data via Site-Wise De-Meaning , 2021, Frontiers in Computational Neuroscience.
[63] A. Leemans,et al. Diffusion MRI harmonization enables joint-analysis of multicentre data of patients with cerebral small vessel disease , 2021, NeuroImage: Clinical.
[64] F. Tixier,et al. Evaluation of conventional and deep learning based image harmonization methods in radiomics studies , 2021, Physics in medicine and biology.
[65] M. Hatt,et al. Development of a Radiomic-Based Model Predicting Lymph Node Involvement in Prostate Cancer Patients , 2021, Cancers.
[66] Seong Jae Hwang,et al. A multi-scanner neuroimaging data harmonization using RAVEL and ComBat , 2021, NeuroImage.
[67] Jie Zhang,et al. Cross-Vendor CT Image Data Harmonization Using CVH-CT , 2021, AMIA.
[68] T. Nir,et al. Diffusion MRI metrics and their relation to dementia severity: effects of harmonization approaches , 2021, Symposium on Medical Information Processing and Analysis.
[69] V. Schmithorst,et al. Harmonization of Multi-Center Diffusion Tensor Tractography in Neonates with Congenital Heart Disease: Optimizing Post-Processing and Application of ComBat , 2021, medRxiv.
[70] Osamu Abe,et al. Cross-scanner reproducibility and harmonization of a diffusion MRI structural brain network: A traveling subject study of multi-b acquisition , 2021, NeuroImage.
[71] M. Giger,et al. Multi-Stage Harmonization for Robust AI across Breast MR Databases , 2021, Cancers.
[72] J. Morris,et al. Deep Generative Medical Image Harmonization for Improving Cross‐Site Generalization in Deep Learning Predictors , 2021, Journal of magnetic resonance imaging : JMRI.
[73] Jerry L Prince,et al. Unsupervised MR harmonization by learning disentangled representations using information bottleneck theory , 2021, NeuroImage.
[74] Bo Li,et al. IAS-NET: Joint Intra-classly Adaptive GAN and Segmentation network for unsupervised cross-domain in Neonatal Brain MRI segmentation. , 2021, Medical physics.
[75] Eric W. Bridgeford,et al. Eliminating accidental deviations to minimize generalization error and maximize replicability: Applications in connectomics and genomics , 2021, PLoS Comput. Biol..
[76] Saori C. Tanaka,et al. A multi-site, multi-disorder resting-state magnetic resonance image database , 2021, Scientific Data.
[77] Saori C. Tanaka,et al. Comparison of traveling‐subject and ComBat harmonization methods for assessing structural brain characteristics , 2021, Human brain mapping.
[78] P. Thompson,et al. Alzheimer’s Disease Classification Accuracy is Improved by MRI Harmonization based on Attention-Guided Generative Adversarial Networks , 2021, bioRxiv.
[79] Benjamin N. Conrad,et al. MASiVar: Multisite, multiscanner, and multisubject acquisitions for studying variability in diffusion weighted MRI , 2021, Magnetic Resonance in Medicine.
[80] Jordan D Dworkin,et al. Maturity of gray matter structures and white matter connectomes, and their relationship with psychiatric symptoms in youth , 2021, Human brain mapping.
[81] A. Anwander,et al. Same Brain, Different Look?—The Impact of Scanner, Sequence and Preprocessing on Diffusion Imaging Outcome Parameters , 2021, Journal of clinical medicine.
[82] M. Hatt,et al. A transfer learning approach to facilitate ComBat-based harmonization of multicentre radiomic features in new datasets , 2021, PloS one.
[83] Aaron Carass,et al. Autoencoder based self-supervised test-time adaptation for medical image analysis , 2021, Medical Image Anal..
[84] Saori C. Tanaka,et al. Common Brain Networks Between Major Depressive-Disorder Diagnosis and Symptoms of Depression That Are Validated for Independent Cohorts , 2021, Frontiers in Psychiatry.
[85] C. Balleyguier,et al. Impact of Preprocessing and Harmonization Methods on the Removal of Scanner Effects in Brain MRI Radiomic Features , 2021, Cancers.
[86] D. Rangaprakash,et al. Functional Connectivity-Based Prediction of Autism on Site Harmonized ABIDE Dataset , 2021, IEEE Transactions on Biomedical Engineering.
[87] A. Abi-Dargham,et al. Cross‐Scanner Harmonization of Neuromelanin‐Sensitive MRI for Multisite Studies , 2021, Journal of magnetic resonance imaging : JMRI.
[88] Dinggang Shen,et al. Multi-site MRI harmonization via attention-guided deep domain adaptation for brain disorder identification , 2021, Medical Image Anal..
[89] Eva Rothgang,et al. MR-contrast-aware image-to-image translations with generative adversarial networks , 2021, International Journal of Computer Assisted Radiology and Surgery.
[90] Tilo Kircher,et al. The German research consortium for the study of bipolar disorder (BipoLife): a magnetic resonance imaging study protocol , 2021, International Journal of Bipolar Disorders.
[91] N. Jahanshad,et al. Style Transfer Using Generative Adversarial Networks for Multi-Site MRI Harmonization , 2021, bioRxiv.
[92] C. Schwarz. Uses of Human MR and PET Imaging in Research of Neurodegenerative Brain Diseases , 2021, Neurotherapeutics.
[93] Saori C. Tanaka,et al. Brain/MINDS beyond human brain MRI project: A protocol for multi-level harmonization across brain disorders throughout the lifespan , 2021, NeuroImage: Clinical.
[94] X. Buy,et al. Assessment of Repeatability, Reproducibility, and Performances of T2 Mapping‐Based Radiomics Features: A Comparative Study , 2021, Journal of magnetic resonance imaging : JMRI.
[95] W. Johnson,et al. Overcoming the impacts of two-step batch effect correction on gene expression estimation and inference , 2021, bioRxiv.
[96] N. Obuchowski,et al. Incorporating radiomics into clinical trials: expert consensus endorsed by the European Society of Radiology on considerations for data-driven compared to biologically driven quantitative biomarkers , 2021, European Radiology.
[97] Nicola K. Dinsdale,et al. Deep learning-based unlearning of dataset bias for MRI harmonisation and confound removal , 2020, NeuroImage.
[98] Klaus P. Ebmeier,et al. Integrating large-scale neuroimaging research datasets: Harmonisation of white matter hyperintensity measurements across Whitehall and UK Biobank datasets , 2020, NeuroImage.
[99] Christian Wachinger,et al. Detect and Correct Bias in Multi-Site Neuroimaging Datasets , 2020, Medical Image Anal..
[100] David Rossell,et al. Heterogeneous Large Datasets Integration Using Bayesian Factor Regression , 2018, Bayesian Analysis.
[101] OUP accepted manuscript , 2021, Biostatistics.
[102] Jenny Benois-Pineau,et al. Improving Alzheimer's stage categorization with Convolutional Neural Network using transfer learning and different magnetic resonance imaging modalities , 2020, Heliyon.
[103] Saori C. Tanaka,et al. Generalizable brain network markers of major depressive disorder across multiple imaging sites , 2020, PLoS biology.
[104] Uwe Oelfke,et al. Cross-modality deep learning: Contouring of MRI data from annotated CT data only. , 2020, Medical physics.
[105] Matcheri Keshavan,et al. Exploring the limits of ComBat method for multi-site diffusion MRI harmonization , 2020, bioRxiv.
[106] Fanny Orlhac,et al. A radiomics pipeline dedicated to Breast MRI: validation on a multi-scanner phantom study , 2020, Magnetic Resonance Materials in Physics, Biology and Medicine.
[107] Saad Jbabdi,et al. Challenges and future directions for representations of functional brain organization , 2020, Nature Neuroscience.
[108] Peter A. Calabresi,et al. A Disentangled Latent Space for Cross-Site MRI Harmonization , 2020, MICCAI.
[109] Fanny Orlhac,et al. How can we combat multicenter variability in MR radiomics? Validation of a correction procedure , 2020, European Radiology.
[110] Olivier Saut,et al. Intensity harmonization techniques influence radiomics features and radiomics-based predictions in sarcoma patients , 2020, Scientific Reports.
[111] Jianhui Zhong,et al. A deep learning-based method for improving reliability of multicenter diffusion kurtosis imaging with varied acquisition protocols. , 2020, Magnetic resonance imaging.
[112] Dimitris Visvikis,et al. Harmonization strategies for multicenter radiomics investigations , 2020, Physics in medicine and biology.
[113] F. Sepehrband,et al. Cross-scanner and cross-protocol multi-shell diffusion MRI data harmonization: Algorithms and results , 2020, NeuroImage.
[114] X. Shen,et al. A hitchhiker’s guide to working with large, open-source neuroimaging datasets , 2020, Nature Human Behaviour.
[115] Joanne C. Beer,et al. Longitudinal ComBat: A method for harmonizing longitudinal multi-scanner imaging data☆ , 2020, NeuroImage.
[116] A. Mechelli,et al. Neuroharmony: A new tool for harmonizing volumetric MRI data from unseen scanners , 2020, NeuroImage.
[117] S. Sisodiya,et al. Valproate Use Is Associated With Posterior Cortical Thinning and Ventricular Enlargement in Epilepsy Patients , 2020, Frontiers in Neurology.
[118] Eva Friedel,et al. Simulating ComBat: how batch correction can lead to the systematic introduction of false positive results in DNA methylation microarray studies , 2020, BMC Bioinformatics.
[119] M. Hatt,et al. Performance comparison of modified ComBat for harmonization of radiomic features for multicenter studies , 2020, Scientific Reports.
[120] Tommy Löfstedt,et al. Latent Space Manipulation for High-Resolution Medical Image Synthesis via the StyleGAN. , 2020, Zeitschrift fur medizinische Physik.
[121] Russell T. Shinohara,et al. Increased power by harmonizing structural MRI site differences with the ComBat batch adjustment method in ENIGMA , 2020, NeuroImage.
[122] Lars T. Westlye,et al. Hierarchical Bayesian Regression for Multi-Site Normative Modeling of Neuroimaging Data , 2020, MICCAI.
[123] Andrew L. Alexander,et al. A 3D Fully Convolutional Neural Network With Top-Down Attention-Guided Refinement for Accurate and Robust Automatic Segmentation of Amygdala and Its Subnuclei , 2020, Frontiers in Neuroscience.
[124] W. Tseng,et al. Generalization of diffusion magnetic resonance imaging–based brain age prediction model through transfer learning , 2020, NeuroImage.
[125] Jan Sijbers,et al. Harmonization of Brain Diffusion MRI: Concepts and Methods , 2020, Frontiers in Neuroscience.
[126] Kok Siong Ang,et al. A benchmark of batch-effect correction methods for single-cell RNA sequencing data , 2020, Genome Biology.
[127] Xinbo Gao,et al. Inter-site harmonization based on dual generative adversarial networks for diffusion tensor imaging: application to neonatal white matter development , 2020, BioMedical Engineering OnLine.
[128] Maryellen L. Giger,et al. Harmonization of radiomic features of breast lesions across international DCE-MRI datasets , 2020, Journal of medical imaging.
[129] Christos Davatzikos,et al. Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan , 2019, NeuroImage.
[130] Douglas L. Arnold,et al. Deep learning segmentation of orbital fat to calibrate conventional MRI for longitudinal studies , 2019, NeuroImage.
[131] Jung-Woo Ha,et al. StarGAN v2: Diverse Image Synthesis for Multiple Domains , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[132] Richard G. Wise,et al. Multi-site harmonization of 7 tesla MRI neuroimaging protocols , 2019, NeuroImage.
[133] A. Simmons,et al. The reliability of a deep learning model in clinical out-of-distribution MRI data: a multicohort study , 2019, Medical Image Anal..
[134] J Wrobel,et al. Intensity warping for multisite MRI harmonization , 2019, NeuroImage.
[135] Paul M. Thompson,et al. Scanner invariant representations for diffusion MRI harmonization , 2019, Magnetic resonance in medicine.
[136] AmanPreet Badhwar,et al. Multivariate consistency of resting-state fMRI connectivity maps acquired on a single individual over 2.5 years, 13 sites and 3 vendors , 2018, NeuroImage.
[137] Tolga Çukur,et al. A Transfer‐Learning Approach for Accelerated MRI Using Deep Neural Networks , 2017, Magnetic resonance in medicine.
[138] Neda Bernasconi,et al. White matter abnormalities across different epilepsy syndromes in adults: an ENIGMA Epilepsy study , 2019, bioRxiv.
[139] Aaron Carass,et al. DeepHarmony: A deep learning approach to contrast harmonization across scanner changes. , 2019, Magnetic resonance imaging.
[140] Dimitris Visvikis,et al. Standardization of Multicentric Image Datasets with Generative Adversarial Networks , 2019 .
[141] Li Wang,et al. Harmonization of Infant Cortical Thickness Using Surface-to-Surface Cycle-Consistent Adversarial Networks , 2019, MICCAI.
[142] Timothy O. Laumann,et al. Identifying reproducible individual differences in childhood functional brain networks: An ABCD study , 2019, Developmental Cognitive Neuroscience.
[143] Yogesh Rathi,et al. White matter abnormalities across the lifespan of schizophrenia: a harmonized multi-site diffusion MRI study , 2019, Molecular Psychiatry.
[144] Fan Zhang,et al. Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation , 2019, MICCAI.
[145] Christos Davatzikos,et al. Neuroimaging Findings in US Government Personnel With Possible Exposure to Directional Phenomena in Havana, Cuba. , 2019, JAMA.
[146] Jelle Veraart,et al. Cross-scanner and cross-protocol diffusion MRI data harmonisation: A benchmark database and evaluation of algorithms , 2019, NeuroImage.
[147] Nicu Sebe,et al. Attention-Guided Generative Adversarial Networks for Unsupervised Image-to-Image Translation , 2019, 2019 International Joint Conference on Neural Networks (IJCNN).
[148] Qing Ma,et al. Reproducibility of functional brain alterations in major depressive disorder: Evidence from a multisite resting-state functional MRI study with 1,434 individuals , 2019, NeuroImage.
[149] Timo Aila,et al. A Style-Based Generator Architecture for Generative Adversarial Networks , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[150] Lei Wang,et al. Quantitative assessment of field strength, total intracranial volume, sex, and age effects on the goodness of harmonization for volumetric analysis on the ADNI database , 2018, Human brain mapping.
[151] Anders M. Dale,et al. Image processing and analysis methods for the Adolescent Brain Cognitive Development Study , 2018, NeuroImage.
[152] Yogesh Rathi,et al. Retrospective harmonization of multi-site diffusion MRI data acquired with different acquisition parameters , 2019, NeuroImage.
[153] Aykut Erdem,et al. Image Synthesis in Multi-Contrast MRI With Conditional Generative Adversarial Networks , 2018, IEEE Transactions on Medical Imaging.
[154] Zhiwei Li,et al. Fast and Robust Diffusion Kurtosis Parametric Mapping Using a Three-Dimensional Convolutional Neural Network , 2019, IEEE Access.
[155] Thomas E. Nichols,et al. Extending the Human Connectome Project across ages: Imaging protocols for the Lifespan Development and Aging projects , 2018, NeuroImage.
[156] Paul M. Thompson,et al. Diffusion MRI Indices and Their Relation to Cognitive Impairment in Brain Aging: The Updated Multi-protocol Approach in ADNI3 , 2018, bioRxiv.
[157] Saori C. Tanaka,et al. Harmonization of resting-state functional MRI data across multiple imaging sites via the separation of site differences into sampling bias and measurement bias , 2018, bioRxiv.
[158] M. Weissman,et al. Statistical harmonization corrects site effects in functional connectivity measurements from multi‐site fMRI data , 2018, Human brain mapping.
[159] Adam G. Thomas,et al. Detecting and harmonizing scanner differences in the ABCD study - annual release 1.0 , 2018, bioRxiv.
[160] Avshalom Caspi,et al. All for One and One for All: Mental Disorders in One Dimension. , 2018, The American journal of psychiatry.
[161] Anders M. Dale,et al. The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites , 2018, Developmental Cognitive Neuroscience.
[162] Russell T. Shinohara,et al. Harmonization of cortical thickness measurements across scanners and sites , 2017, NeuroImage.
[163] D. Reich,et al. Volumetric Analysis from a Harmonized Multisite Brain MRI Study of a Single Subject with Multiple Sclerosis , 2017, American Journal of Neuroradiology.
[164] Christos Davatzikos,et al. Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity , 2017, NeuroImage.
[165] Sepp Hochreiter,et al. GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium , 2017, NIPS.
[166] J. Rohrer,et al. Imaging and fluid biomarkers in frontotemporal dementia , 2017, Nature Reviews Neurology.
[167] Serge J. Belongie,et al. Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[168] Ragini Verma,et al. Harmonization of multi-site diffusion tensor imaging data , 2017, NeuroImage.
[169] Theo G. M. van Erp,et al. Multisite reliability of MR-based functional connectivity , 2017, NeuroImage.
[170] Peter Savadjiev,et al. Multi-site harmonization of diffusion MRI data in a registration framework , 2017, Brain Imaging and Behavior.
[171] Peter Savadjiev,et al. Inter-site and inter-scanner diffusion MRI data harmonization , 2016, NeuroImage.
[172] Anisha Keshavan,et al. Power estimation for non-standardized multisite studies , 2016, NeuroImage.
[173] Russell T. Shinohara,et al. Removing inter-subject technical variability in magnetic resonance imaging studies , 2016, NeuroImage.
[174] Paul J. Harrison,et al. Oxford Lithium Trial (OxLith) of the early affective, cognitive, neural and biochemical effects of lithium carbonate in bipolar disorder: study protocol for a randomised controlled trial , 2016, Trials.
[175] Ludovico Minati,et al. Longitudinal reproducibility of default-mode network connectivity in healthy elderly participants: A multicentric resting-state fMRI study , 2016, NeuroImage.
[176] E. Hovig,et al. Methods that remove batch effects while retaining group differences may lead to exaggerated confidence in downstream analyses , 2015, Biostatistics.
[177] Honglak Lee,et al. Learning Structured Output Representation using Deep Conditional Generative Models , 2015, NIPS.
[178] Stephen M. Smith,et al. ICA-based artifact removal diminishes scan site differences in multi-center resting-state fMRI , 2015, Front. Neurosci..
[179] Peter Savadjiev,et al. Harmonizing Diffusion MRI Data Across Multiple Sites and Scanners , 2015, MICCAI.
[180] F. Dekker,et al. Graphical presentation of confounding in directed acyclic graphs. , 2015, Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association.
[181] D. Auer,et al. Multi-centre reproducibility of diffusion MRI parameters for clinical sequences in the brain , 2015, NMR in biomedicine.
[182] T. Scheenen,et al. Multi‐center reproducibility of neurochemical profiles in the human brain at 7 T , 2015, NMR in biomedicine.
[183] Nick C Fox,et al. Imaging endpoints for clinical trials in Alzheimer’s disease , 2014, Alzheimer's Research & Therapy.
[184] Krzysztof J. Gorgolewski,et al. Making big data open: data sharing in neuroimaging , 2014, Nature Neuroscience.
[185] C. Crainiceanu,et al. Statistical normalization techniques for magnetic resonance imaging , 2014, NeuroImage: Clinical.
[186] Steen Moeller,et al. ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging , 2014, NeuroImage.
[187] Michelle L. McGowan,et al. Big data, open science and the brain: lessons learned from genomics , 2014, Front. Hum. Neurosci..
[188] Gary H. Glover,et al. A multi-scanner study of subcortical brain volume abnormalities in schizophrenia , 2014, Psychiatry Research: Neuroimaging.
[189] Ludovica Griffanti,et al. Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers , 2014, NeuroImage.
[190] Christos Davatzikos,et al. Neuroimaging of the Philadelphia Neurodevelopmental Cohort , 2014, NeuroImage.
[191] Naoto Hayashi,et al. Effects of study design in multi-scanner voxel-based morphometry studies , 2014, NeuroImage.
[192] Daniel P. Kennedy,et al. The Autism Brain Imaging Data Exchange: Towards Large-Scale Evaluation of the Intrinsic Brain Architecture in Autism , 2013, Molecular Psychiatry.
[193] Paul M. Thompson,et al. Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: A pilot project of the ENIGMA–DTI working group , 2013, NeuroImage.
[194] Essa Yacoub,et al. The WU-Minn Human Connectome Project: An overview , 2013, NeuroImage.
[195] Timothy D Johnson,et al. Multi‐system repeatability and reproducibility of apparent diffusion coefficient measurement using an ice‐water phantom , 2013, Journal of magnetic resonance imaging : JMRI.
[196] K. Ohtomo,et al. Effect of scanner in longitudinal studies of brain volume changes , 2011, Journal of magnetic resonance imaging : JMRI.
[197] C. Jack,et al. Chronic divalproex sodium to attenuate agitation and clinical progression of Alzheimer disease. , 2011, Archives of general psychiatry.
[198] Brian B. Avants,et al. N4ITK: Improved N3 Bias Correction , 2010, IEEE Transactions on Medical Imaging.
[199] Fei-Fei Li,et al. ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[200] T. Trikalinos,et al. Are unadjusted analyses of clinical trials inappropriately biased toward the null? , 2009, Stroke.
[201] P. Bath,et al. Should Stroke Trials Adjust Functional Outcome for Baseline Prognostic Factors? , 2009, Stroke.
[202] Nimodipine BI Orgo,et al. Should Stroke Trials Adjust Functional Outcome for Baseline Prognostic Factors ? , 2009 .
[203] P. Hluštík,et al. Effects of spatial smoothing on fMRI group inferences. , 2008, Magnetic resonance imaging.
[204] Clifford R. Jack,et al. Interpreting scan data acquired from multiple scanners: A study with Alzheimer's disease , 2008, NeuroImage.
[205] Brian B. Avants,et al. Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain , 2008, Medical Image Anal..
[206] Cheng Li,et al. Adjusting batch effects in microarray expression data using empirical Bayes methods. , 2007, Biostatistics.
[207] Anders M. Dale,et al. Reliability of MRI-derived measurements of human cerebral cortical thickness: The effects of field strength, scanner upgrade and manufacturer , 2006, NeuroImage.
[208] Anders M. Dale,et al. Reliability in multi-site structural MRI studies: Effects of gradient non-linearity correction on phantom and human data , 2006, NeuroImage.
[209] Ludwig Kappos,et al. A randomized, placebo-controlled trial of natalizumab for relapsing multiple sclerosis. , 2006, The New England journal of medicine.
[210] Ewout W Steyerberg,et al. Randomized controlled trials with time-to-event outcomes: how much does prespecified covariate adjustment increase power? , 2006, Annals of epidemiology.
[211] M. Höfler,et al. Causal inference based on counterfactuals , 2005, BMC medical research methodology.
[212] C. Jack,et al. Ways toward an early diagnosis in Alzheimer’s disease: The Alzheimer’s Disease Neuroimaging Initiative (ADNI) , 2005, Alzheimer's & Dementia.
[213] Ewout W Steyerberg,et al. Covariate adjustment in randomized controlled trials with dichotomous outcomes increases statistical power and reduces sample size requirements. , 2004, Journal of clinical epidemiology.
[214] Scott T. Grafton,et al. Sharing neuroimaging studies of human cognition , 2004, Nature Neuroscience.
[215] Eero P. Simoncelli,et al. Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.
[216] T. Ihalainen,et al. MRI quality control: six imagers studied using eleven unified image quality parameters , 2004, European Radiology.
[217] Pierre Hellier,et al. Consistent intensity correction of MR images , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).
[218] L. K. Hansen,et al. Independent component analysis of functional MRI: what is signal and what is noise? , 2003, Current Opinion in Neurobiology.
[219] Stephen M Smith,et al. Fast robust automated brain extraction , 2002, Human brain mapping.
[220] Michael Brady,et al. Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.
[221] L G Nyúl,et al. On standardizing the MR image intensity scale , 1999, Magnetic resonance in medicine.
[222] J. Neuhaus. Estimation efficiency with omitted covariates in generalized linear models , 1998 .
[223] David H. Miller,et al. Correction for variations in MRI scanner sensitivity in brain studies with histogram matching , 1998, Magnetic resonance in medicine.
[224] S Makeig,et al. Analysis of fMRI data by blind separation into independent spatial components , 1998, Human brain mapping.
[225] D. Rubin,et al. The central role of the propensity score in observational studies for causal effects , 1983 .
[226] M. Graffar. [Modern epidemiology]. , 1971, Bruxelles medical.